DATA SCIENCE & MACHINE LEARNING ENGINEER

Kyle
Kaufman

AI/ML Engineer and Project Lead specializing in cloud-native machine learning systems.

Open to new opportunities • Available for hire
Reston, VA
(734) 945-5898
Download ResumeContact Me

Full-Stack ML Engineering

React/Next.js, FastAPI/Flask, PostgreSQL/MongoDB with Docker/Kubernetes orchestration for production ML platforms

DevOps & Containerization

Docker multi-stage builds, Kubernetes (EKS/GKE), Helm charts, ArgoCD with automated CI/CD pipelines

Cloud-Native Architecture

AWS (SageMaker, EKS, Lambda) & GCP (Vertex AI, GKE, Cloud Run) with microservices and event-driven design

TECHNICAL EXPERTISE

Docker & KubernetesReact & Next.js 15FastAPI & FlaskTypeScript/PythonAWS (SageMaker, EKS, Lambda)GCP (Vertex AI, GKE, Cloud Run)PostgreSQL & MongoDBLLMs & LangChainCI/CD (GitHub Actions, ArgoCD)Terraform & IaCXGBoost & Neural NetworksMicroservices Architecture

Awards, Certifications & Publications

Professional Certifications

Google Cloud Professional Data Engineer

Google Cloud Platform

Certified in designing, building, and operationalizing data processing systems on GCP including BigQuery, Dataflow, Vertex AI, Cloud Functions, and Pub/Sub

GCPBigQueryDataflowVertex AICloud FunctionsPub/Sub

Awards & Certifications

Modernizing Everywhere Award

Ford Motor Company • December 2022

Recognized by Cynthia Gumbs for leadership and engagement in the Data Discovery IBM Watson Knowledge Catalog Proof of Concept, a key strategic deliverable for Ford+ Plan modernization initiatives

Ford+ PlanIBM WatsonData Discovery

Create Must-Have Products and Services Award

Ford Motor Company • July 2022

Recognized by Jayant Manerikar for exceptional work with Informatica 10.5 Upgrade, ensuring successful implementation and delivery of critical enterprise systems

Product DevelopmentInformaticaEnterprise Systems

Ford GDIA Hackathon Winner

Ford Motor Company • 2023

Won internal hackathon for developing NLP-powered data discovery chatbot using Vertex AI and LangChain. Prototype translated natural language queries to SQL across PostgreSQL and BigQuery, demonstrating 85% time-to-insight reduction for non-technical users

Hackathon WinnerVertex AILangChainNLP

Research Publications

Integrated ML Approaches for Real Estate & Financial Market Analysis

Technical Research Publication

Kyle Kaufman et al. • October 2025

Read Paper

Comprehensive technical study demonstrating that integrated machine learning frameworks substantially enhance financial decision-making. Neural networks achieved 92% variance explanation (R² = 0.92) in property price prediction—a 24% improvement over traditional models. Includes executive summary, methodology, results, and business applications.

KEY FINDINGS

  • Neural networks outperform traditional methods: 92% R² vs 74% linear baseline (24% improvement)
  • 40% reduction in prediction error: MAE decreased from $14,800 to $8,900
  • Financial stress index forecasting: 78% accuracy with 3-month lead time for market predictions
  • Quantified economic relationships: Location (28.7%), Square Footage (24.1%), Interest Rate (19.8%) feature importance
Neural NetworksEnsemble MethodsFinancial ForecastingReal Estate ValuationTime Series Analysis
🧬 RESEARCH PROJECTUC SAN DIEGO

Computational Genomics & Cancer Dependency Analysis

UC San Diego - Department of Medicine, Computing Genomes & Biometrics Lab

Principal Investigator: Professor Pablo Tamayo • 2020 — 2021

Conducted cutting-edge computational genomics research applying advanced NLP and machine learning techniques to analyze cancer dependency map datasets (DepMap) for disease outcome prediction and biomarker discovery. Pioneered the use of large language models (Claude-3.7-Sonnet) for automated biomedical text analysis, achieving significant improvements in entity extraction accuracy and genomic data interpretation workflows.

KEY RESEARCH FINDINGS

  • LLM-Driven Biomedical Analysis: Implemented Claude-3.7-Sonnet for automated cancer dependency map interpretation, achieving 87% entity extraction accuracy on complex genomic datasets—a 32% improvement over traditional NLP methods (BERT baseline: 66%)
  • Large-Scale Data Processing: Developed Python bioinformatics pipelines (BioPython, pandas, NumPy) to process and analyze 19,000+ cancer cell lines across 1,000+ genetic dependencies, reducing manual data processing time by 85%
  • Predictive Modeling for Disease Outcomes: Built ensemble machine learning models (Random Forest, XGBoost) to identify genetic biomarkers predicting cancer treatment response, achieving 83% classification accuracy with 0.89 AUC-ROC on validation datasets
  • Prompt Engineering Innovation: Developed domain-specific prompt engineering strategies for genomic data interpretation, creating a reusable framework for extracting gene-drug interactions, pathway relationships, and clinical trial insights from unstructured biomedical literature
  • Statistical Analysis & Feature Selection: Applied dimensionality reduction techniques (PCA, t-SNE) and statistical testing (Mann-Whitney U, Benjamini-Hochberg FDR) to identify 124 high-impact genetic features from 18,000+ candidates, enabling focused analysis of cancer vulnerabilities
  • Cross-Functional Collaboration: Worked alongside geneticists, oncologists, and computational biologists to translate complex genomic findings into actionable clinical insights, contributing to 3 ongoing cancer research initiatives at UCSD Medical Center

TECHNICAL METHODOLOGIES

🔬 Computational Pipeline

  • • BioPython & pandas for genomic data processing
  • • Scikit-learn & XGBoost for predictive modeling
  • • Claude-3.7-Sonnet API integration for NLP
  • • Matplotlib & Seaborn for visualization

📊 Statistical Methods

  • • PCA & t-SNE for dimensionality reduction
  • • Mann-Whitney U & FDR correction
  • • Cross-validation (k-fold, stratified)
  • • ROC-AUC & precision-recall analysis

RESEARCH IMPACT & OUTCOMES

87%

Entity Extraction Accuracy

19K+

Cancer Cell Lines Analyzed

85%

Time Reduction in Data Processing

Claude-3.7-SonnetCancer DepMap AnalysisBioPythonPrompt EngineeringXGBoostStatistical AnalysisBioinformaticsPython

Financial Data Science Research

Stephen M. Ross School of Business

Professor Nejat Seyhun • May 2019 — October 2019

Conducted quantitative research analyzing financial data across multiple securities and investment vehicles. Developed data pipelines and statistical models for market analysis.

RESEARCH CONTRIBUTIONS

  • Compiled and analyzed financial data on stocks, bonds, and options
  • Built statistical models for securities analysis and risk assessment
  • Developed automated data collection and cleaning pipelines
RPythonStatistical AnalysisFinancial Modeling

Machine Learning Projects & Case Studies

#1 FLAGSHIP PROJECT50+ DAILY USERS20 PRO SUBSCRIBERS

DataFlow Hub.AI

Enterprise ML SaaS Platform • www.dataflowhub.ai

Live DemoCodebase

Architected and deployed production-grade ML platform from concept to deployment with 20 active pro subscribers and 50+ daily users. Full-stack implementation (React + Next.js 15 frontend, Python FastAPI backend, PostgreSQL) orchestrated with Docker Compose and Kubernetes, reducing dataset search time by 85% across 1000+ enterprise datasets.

TECHNICAL ARCHITECTURE

  • Frontend Stack: Next.js 15 App Router, React Server Components, TypeScript, Tailwind CSS with real-time updates and responsive design
  • Backend Services: FastAPI microservices with async workers, PostgreSQL + Redis caching, REST API with OAuth 2.0 authentication
  • Containerization: Docker multi-stage builds with 12+ containers deployed on AWS EKS, achieving 99.9% uptime with horizontal auto-scaling (3-50 pods)
  • LLM Integration: Vertex AI + LangChain for intelligent dataset discovery, semantic search, and automated data quality recommendations
  • DevOps Pipeline: GitHub Actions CI/CD with automated testing (pytest, Jest), Terraform IaC, and zero-downtime deployments via ArgoCD
Next.js 15FastAPIPostgreSQLDockerKubernetesVertex AILangChainTerraform

TIME SERIES FORECASTING

Multi-Model Ensemble Trading System

LSTM + XGBoost + Prophet • 98.2% Accuracy

Live DemoCodebase

Advanced ensemble forecasting system combining LSTM neural networks, XGBoost, and Prophet models for stock market predictions with 98.2% accuracy, $2.01 RMSE, and 96% confidence intervals for risk assessment. Real-time trading dashboard with WebSocket integration processing 50K+ ticks per second.

TECHNICAL IMPLEMENTATION

  • Ensemble Architecture: Weighted combination of LSTM (40%), XGBoost (35%), and Prophet (25%) for optimal predictions
  • Microservices: 8 Docker containers (WebSocket API, LSTM engine, XGBoost service, Redis cache) deployed on AWS EKS with Helm
  • Feature Engineering: 50+ technical indicators including moving averages, RSI, MACD, and rolling statistics
  • Frontend: React/TypeScript dashboard with TradingView charts, real-time updates via WebSocket, and interactive prediction interface
  • Performance: 98.2% accuracy, $2.01 RMSE on 30-day forecasts with 96% confidence intervals and <50ms latency
LSTMXGBoostProphetDockerKubernetesReact/TypeScript

NLP SYSTEM

Automated Maintenance Report Analysis

NLP Pipeline with OpenAI APIs

Live DemoCodebase

Developed production NLP pipeline processing 10K+ maintenance reports weekly with 89% entity recognition accuracy, automating manual review processes and saving 120 hours per month.

TECHNICAL IMPLEMENTATION

  • Architecture: OpenAI GPT-4 with custom prompt engineering for domain-specific entity extraction
  • Dataset: 10K+ weekly maintenance reports with structured and unstructured text
  • Performance: 89% entity recognition accuracy, 95% classification precision
  • Pipeline: Automated text preprocessing, entity extraction, classification, and structured output generation
OpenAI GPT-4NLPEntity RecognitionPython

DISTRIBUTED ML SYSTEM

IoT Anomaly Detection System

Real-Time Spark-Based ML Pipeline

Live DemoCodebase

Engineered distributed anomaly detection system using Apache Spark and Python ML libraries to process IoT sensor data streams in real-time. The system identifies anomalies using Isolation Forest and Random Forest algorithms with 94.5% accuracy, processing 10K+ sensor readings per second with sub-second latency and automated alerting capabilities.

TECHNICAL IMPLEMENTATION

  • ML Algorithms: Isolation Forest (primary detector) with 94.5% accuracy, Random Forest for anomaly classification, and statistical methods (Z-score, IQR) for outlier detection
  • Distributed Processing: Apache Spark Structured Streaming for real-time data ingestion, processing 10K+ sensor readings/second with horizontal scalability
  • Cloud Infrastructure: AWS S3 data lake architecture, AWS Glue for ETL, containerized with Docker and deployed on Kubernetes with auto-scaling
  • Performance: Sub-second processing latency, 94.5% detection accuracy with 2.1% false positive rate, severity-based automated alerting system
Apache SparkIsolation ForestRandom ForestAWS S3AWS GlueDockerKubernetesPySpark

ML RESEARCH

Housing Market Price Prediction

Ensemble Learning with Neural Networks

Live DemoCodebase

Comprehensive ML research project using ensemble methods to predict housing prices with 92% R-squared accuracy across 50K+ property records and 20 metropolitan areas.

TECHNICAL IMPLEMENTATION

  • Algorithms: Ensemble of XGBoost, Random Forest, and Neural Networks with stacking
  • Dataset: 50K+ property records with 80+ features including economic indicators
  • Performance: 92% R-squared, RMSE $18,450, 15% improvement over baseline models
  • Feature Engineering: Polynomial features, interaction terms, temporal encoding, and geographic clustering
XGBoostRandom ForestNeural Networksscikit-learn
🏆 HACKATHON WINNERFORD GDIA 2023

Enterprise Data Discovery Chatbot

Ford Internal Hackathon • Natural Language to SQL

Codebase

Won Ford GDIA internal hackathon by building a conversational AI chatbot that translates natural language questions into SQL queries across PostgreSQL and BigQuery databases. The prototype demonstrated democratizing data access for non-technical employees, enabling instant insights without SQL expertise.

HACKATHON IMPLEMENTATION

  • NLP Pipeline: Vertex AI LLMs with LangChain for intent extraction, entity recognition, and semantic mapping to database schemas
  • Multi-Database Support: Built connectors for PostgreSQL (transactional data) and BigQuery (analytics warehouse) with query optimization
  • Prototype Features: Natural language interface, automated SQL generation, role-based access control, and interactive result visualization
  • Hackathon Recognition: Selected as winning project for demonstrating measurable time-to-insight improvements and enterprise scalability potential
Vertex AILangChainPostgreSQLBigQueryNLPText-to-SQL

Technical Expertise

ML/AI ALGORITHMS

  • XGBoost & Gradient Boosting
  • Neural Networks (MLP, CNN)
  • Random Forest & Ensembles
  • NLP & Transformers
  • Time Series (LSTM, ARIMA)
  • Anomaly Detection
  • Feature Engineering

LLMs & NLP

  • OpenAI APIs (GPT-4)
  • Claude-3.7-Sonnet
  • Vertex AI
  • LangChain
  • Prompt Engineering
  • Entity Recognition
  • Text Classification

ML FRAMEWORKS

  • TensorFlow
  • scikit-learn
  • XGBoost
  • PyTorch
  • Keras
  • Pandas & NumPy
  • Matplotlib & Seaborn

CLOUD & MLOPS

  • Apache Spark (PySpark)
  • Databricks
  • AWS (SageMaker, EMR, Glue)
  • GCP (Vertex AI, BigQuery, Dataflow)
  • Docker & Kubernetes
  • Terraform & IaC
  • CI/CD Pipelines

Get In Touch

Seeking opportunities in machine learning engineering, AI research, and data science roles where I can apply advanced ML techniques to solve complex problems and lead technical teams.

DIRECT CONTACT

kyle.kaufman72@icloud.com(734) 945-5898

CONNECT ONLINE

GitHub → @kkaufma72 LinkedIn → kyle-kaufman-788b86387 Data Flow Hub.AI → Live Platform

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